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Speed up find-german-comments: speed up text_cat -s

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follow-up commit to https://gerrit.libreoffice.org/#/c/9226/ .

text_cat -s was documented as "Not very efficient yet, because
language models are re-loaded after each line." So if we want
to use text_cat -s for thousands of lines, better
read the language models only once and store them.

When tested on svl/source, the speed-up was a futher factor 1.5
(reduced from 6 s to 4 s).

Change-Id: I654a250b0e369e01c5eac5970b64df1390f0ef35
Reviewed-on: https://gerrit.libreoffice.org/9227
Reviewed-by: Michael Meeks <michael.meeks@collabora.com>
Tested-by: Michael Meeks <michael.meeks@collabora.com>
This commit is contained in:
Jeroen Nijhof
2014-05-01 19:32:17 +02:00
committed by Michael Meeks
parent 6efd972591
commit cda4ee0c50
1 changed files with 47 additions and 36 deletions
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67
bin/text_cat/text_cat
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@@ -8,6 +8,8 @@ use Getopt::Std;
use Benchmark; use Benchmark;
my $non_word_characters='0-9\s'; my $non_word_characters='0-9\s';
my @languages; # languages (sorted by name)
my %ngram_for; # map language x ngram => rang
# OPTIONS # OPTIONS
getopts('a:d:f:hi:lnst:u:v'); getopts('a:d:f:hi:lnst:u:v');
@@ -94,31 +96,11 @@ if ($opt_n) {
classify(input()); classify(input());
} }
# CLASSIFICATION sub read_model {
sub classify { my ($file) = @_;
my ($input)=@_; open(LM,"$file") or die "cannot open $file: $!\n";
my %results=(); my %ngram;
my $maxp = $opt_t;
# open directory to find which languages are supported
opendir DIR, "$opt_d" or die "directory $opt_d: $!\n";
my @languages = sort(grep { s/\.lm// && -r "$opt_d/$_.lm" } readdir(DIR));
closedir DIR;
@languages or die "sorry, can't read any language models from $opt_d\n" .
"language models must reside in files with .lm ending\n";
# create ngrams for input. Note that hash %unknown is not used;
# it contains the actual counts which are only used under -n: creating
# new language model (and even then they are not really required).
my @unknown=create_lm($input);
# load model and count for each language.
my $language;
my $t1 = new Benchmark;
foreach $language (@languages) {
# loads the language model into hash %$language.
my %ngram=();
my $rang = 1; my $rang = 1;
open(LM,"$opt_d/$language.lm") || die "cannot open $language.lm: $!\n";
while (<LM>) { while (<LM>) {
chomp; chomp;
# only use lines starting with appropriate character. Others are # only use lines starting with appropriate character. Others are
@@ -127,14 +109,43 @@ sub classify {
$ngram{$&} = $rang++; $ngram{$&} = $rang++;
} }
} }
close(LM); return \%ngram;
#print STDERR "loaded language model $language\n" if $opt_v; }
sub read_models {
# open directory to find which languages are supported
opendir DIR, "$opt_d" or die "directory $opt_d: $!\n";
@languages = sort(grep { s/\.lm// && -r "$opt_d/$_.lm" } readdir(DIR));
closedir DIR;
@languages or die "sorry, can't read any language models from $opt_d\n" .
"language models must reside in files with .lm ending\n";
foreach my $language (@languages) {
$ngram_for{$language} = read_model("$opt_d/$language.lm");
}
}
# CLASSIFICATION
sub classify {
my ($input)=@_;
my %results=();
my $maxp = $opt_t;
read_models() if !@languages;
# create ngrams for input. Note that hash %unknown is not used;
# it contains the actual counts which are only used under -n: creating
# new language model (and even then they are not really required).
my @unknown=create_lm($input);
my $t1 = new Benchmark;
foreach my $language (@languages) {
# compares the language model with input ngrams list # compares the language model with input ngrams list
my $ngram = $ngram_for{$language} or die "no ngrams for $language";
my ($i,$p)=(0,0); my ($i,$p)=(0,0);
while ($i < @unknown) { while ($i < @unknown) {
if ($ngram{$unknown[$i]}) { if ($ngram->{$unknown[$i]}) {
$p=$p+abs($ngram{$unknown[$i]}-$i); $p=$p+abs($ngram->{$unknown[$i]}-$i);
} else { } else {
$p=$p+$maxp; $p=$p+$maxp;
} }